James Oldfield

Orcid: 0000-0002-7000-5179

Affiliations:
  • Queen Mary University of London, School of Electronic Engineering and Computer Science, UK
  • The Cyprus Institute, Computation-based Science and Technology Research Center, Nicosia, Cyprus
  • Goldsmiths University of London, Department of Computing, UK


According to our database1, James Oldfield authored at least 12 papers between 2019 and 2024.

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Bibliography

2024
Multilinear Mixture of Experts: Scalable Expert Specialization through Factorization.
CoRR, 2024

2023
Parts of Speech-Grounded Subspaces in Vision-Language Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

PandA: Unsupervised Learning of Parts and Appearances in the Feature Maps of GANs.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Adversarial Learning of Disentangled and Generalizable Representations of Visual Attributes.
IEEE Trans. Neural Networks Learn. Syst., 2022

ContraCLIP: Interpretable GAN generation driven by pairs of contrasting sentences.
CoRR, 2022

Cluster-guided Image Synthesis with Unconditional Models.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021
Tensor Methods in Computer Vision and Deep Learning.
Proc. IEEE, 2021

Mitigating Demographic Bias in Facial Datasets with Style-Based Multi-attribute Transfer.
Int. J. Comput. Vis., 2021

Tensor Component Analysis for Interpreting the Latent Space of GANs.
Proceedings of the 32nd British Machine Vision Conference 2021, 2021

2020
Shared-Space Autoencoders with Randomized Skip Connections for Building Footprint Detection with Missing Views.
Proceedings of the Pattern Recognition. ICPR International Workshops and Challenges, 2020

Enhancing Facial Data Diversity with Style-based Face Aging.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019
Detecting Early Parkinson's Disease from Keystroke Dynamics using the Tensor-Train Decomposition.
Proceedings of the 27th European Signal Processing Conference, 2019


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